Structure and Aesthetics in Non- Photorealistic Images Hua Li, David Mould, and Jim Davies Carleton University

Artistic or Messed 2/35

Related Work on Evaluating Non- Photorealistic Algorithms Performance based on processing speed – ill-suited for stylization Side-by-side comparisons – not fully convinced by audience 3/35

Perceptual Evaluation on Non- Photorealistic Algorithms Quantitative evaluation – rating scores [Schumann et al. 96, Gooch and Willemsen 02, Mandryk et al. 2011, Mould et al. 2012] – response time [Gooch et al. 04] – eye-tracking data [Mandryk et al. 2011, Mould et al. 2012] Qualitative evaluation – questionnaire-based 4/35

Motivation of Our Study Tone  Structure Tone-based Structure-based Halftoning [Pang et al. 08] [Chang et al. 09] [Ours 10] Stippling [Secord 02] [Mould 07] [Martin et al. 11] [Ours 11] Screening [Ulichney 98] Abstraction [Kyprianidis 11] [Mould 12, 13] [Qu et al. 08] [Ours 11] [Floyd and Steinberg 76] [Ostromou khov 01] 5/35

Questions to Answer Are structural and aesthetic quality related? Do images matter for side-by-side comparisons? 6/35

Participants 30 participants – 15 female and 15 male – 11 artists – aged 18 to 33 7/35

Study Overview 1 ~ 1.5 hours to complete the experiment Using the keyboard or the mouse to enter their responses Tasks: – rating structural and aesthetic quality – collecting response times for rendered images 8/35

Image Stimuli Seven categories – include cars, cats, persons, flowers, buildings, mugs, and birds. Each category contains 13 different images including – one unprocessed image – and 12 rendered images using 12 algorithms. Images are black and white, or greyscale to remove the influence of color. 9/36

Procedure Step 1: verbal introduction Step 2: training Step 3: formal study Step 4: questionnaire Step 5: ranking 10/35

Interfaces Used Interface for collecting the response time11/35

Interfaces Used Aesthetic ratingStructural rating 12/35

Experimental Images -Bird Category Unprocessed 13/35

Experimental Images - Bird Category Structure-Preserving Stippling (SPS) Structure-Aware 14/35

Experimental Images - Bird Category Content-Sensitive Screening (CSS) Structure-Aware 15/35

Experimental Images - Bird Category SPS with Exclusion Masks (SPH) Structure-Aware 16/35

Experimental Images - Bird Category Line Art using edge tangent field (ETF) Structure-Aware 17/35

Experimental Images - Bird Category Artistic Tessellation (AT) Structure-Aware 18/35

Experimental Images - Bird Category Line Art from SPS (Drawing) Structure-Aware 19/35

Experimental Images - Bird Category Secord’s Stippling Method (Secord) Tone- based 20/35

Experimental Images - Bird Category Line Art using edge tangent field (Mmosaics) Tone- based 21/35

Experimental Images - Bird Category Contrast-Aware Halftoning (CAH) 22/35

Experimental Images - Bird Category Black and White (BW) 23/35

Experimental Images - Bird Category Adding 50% salt and pepper noise (Noisy) Reduced Information 24/35

Experimental Images - Bird Category Gaussian filter (Blurring) Reduced Information 25/35

Positive Correlation Between Structural and Aesthetic Ratings 26/35

Dot-based Methods (Stippling) Structure- Aware Tone- based Structure- Aware Tone- based 27/35

Region-based Methods (Mosaics) Structure- Aware Tone- based Structure- Aware Tone- based 28/35

Effect of Category on Ratings 29/35

Effect of Category on Response Time 30/35 Building < Flower < Bird < Cat < Person < Mug < Car

Artists and Non-Artists 31/35

Overall Ranks after Study Participants preferred the AT images (7/30 responses) the most, CAH second (6/30). Participants’ least favorite – blurred images most often (20/30 responses), and with AT second (5/30). Controversial ranking for stylized images rendered by the AT method. 32/35

Conclusions Considering structure as a possible way to increase aesthetic appeal. Considering the choice of the images used. – Generally, bird images were the easiest images to abstract, while Person images were the most difficult. 33/35

Future Work More Participants More Categories More NPR Algorithms Eye tracker 34/35

Thanks for Your Attention. 35/35